Acta Informatica Pragensia 2025, 14(3), 340-364 | DOI: 10.18267/j.aip.2613533

DAC-GCN: A Dual Actor-Critic Graph Convolutional Network with Multi-Hop Aggregation for Enhanced Recommender Systems

Gholamreza Zare ORCID...1, Nima Jafari ORCID...2,3,4, Mehdi Hosseinzadeh ORCID...5, Amir Sahafi ORCID...6
1 Department of Computer Engineering, Qeshm Branch, Islamic Azad University, Qeshm, Iran
2 Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
3 Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Taiwan
4 Research Center of High Technologies and Innovative Engineering, Western Caspian University, Baku, Azerbaijan
5 Pattern Recognition and Machine Learning Laboratory, School of Computing, Gachon University, Seongnam, Republic of Korea
6 Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

Background: Recommender Systems (RSs) frequently face challenges in balancing exploration and exploitation, particularly in dynamic environments where user behaviors evolve over time. Traditional methods struggle to adapt to these complexities, limiting their effectiveness in real-world domains such as e-commerce, streaming services, and social networks.

Objective: The objective of this study is to introduce DAC-GCN, a Dual Actor-Critic Graph Convolutional Network, designed to enhance recommendation accuracy, ranking quality, and adaptability to evolving user preferences. DAC-GCN merges graph-based learning with Deep Reinforcement Learning (DRL) techniques to improve both short-term and long-term user-item interactions.

Methods: DAC-GCN utilizes a dual architecture with separate Graph Convolutional Networks (GCNs) for policy optimization and value estimation. It incorporates Multi-Hop Aggregation (MHA) to capture extended user-item dependencies and an attention mechanism to emphasize pivotal relationships. We evaluate DAC-GCN on benchmark datasets, including MovieLens 100K, MovieLens 1M, Amazon Subscription Boxes, Amazon Magazine Subscriptions, and Mod Cloth, using standard ranking metrics (Precision@K, Recall@K, NDCG@K, MRR@K, and Hit@K).

Results: Experimental results demonstrate that DAC-GCN consistently outperforms state-of-the-art baselines, showing significant improvements in recommendation accuracy, ranking quality, and robustness to shifting user behaviors. The model’s ability to capture complex user-item interactions is greatly enhanced by MHA and attention mechanisms, while the dual architecture ensures training stability.

Conclusion: DAC-GCN offers a scalable, high-performance solution for modern recommender systems, effectively addressing challenges such as data sparsity and changing user preferences. By integrating graph-based methods with DRL, this study advances both the theory and practice of recommender systems and provides valuable insights for future research and practical applications.

Keywords: Recommender system; Graph convolutional network; Actor-critic; Reinforcement learning; Multi-hop aggregation; Personalized recommendations.

Received: October 30, 2024; Revised: February 4, 2025; Accepted: February 6, 2025; Prepublished online: February 19, 2025; Published: August 19, 2025  Show citation

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Zare, G., Jafari, N., Hosseinzadeh, M., & Sahafi, A. (2025). DAC-GCN: A Dual Actor-Critic Graph Convolutional Network with Multi-Hop Aggregation for Enhanced Recommender Systems. Acta Informatica Pragensia, 14(3), 340-364. doi: 10.18267/j.aip.261
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    References

    1. Abnoosian, K., Farnoosh, R., & Behzadi, M. H. (2023). A pipeline -based framework for early prediction of diabetes. Journal of Health and Biomedical Informatics, 10(2), 125-140. https://doi.org/10.34172/jhbmi.2023.19 Go to original source...
    2. Alamdari, P. M., Navimipour, N. J., Hosseinzadeh, M., Safaei, A. A., & Darwesh, A. (2022). Image-based product recommendation method for e-commerce applications using convolutional neural networks. Acta Informatica Pragensia, 11(1), 15-35. https://doi.org/10.18267/j.aip.167 Go to original source...
    3. Albora, G., Mori, L. R., & Zaccaria, A. (2023). Sapling Similarity: A performing and interpretable memory-based tool for recommendation. Knowledge-Based Systems, 275, 110659. https://doi.org/10.1016/j.knosys.2023.110659 Go to original source...
    4. Anjiri, S. N., Ding, D., & Song, Y. (2024). HyGate-GCN: Hybrid-Gate-Based Graph Convolutional Networks with dynamical ratings estimation for personalised POI recommendation. Expert Systems With Applications, 258, 125217. https://doi.org/10.1016/j.eswa.2024.125217 Go to original source...
    5. Chen, C., Zhang, M., Zhang, Y., Liu, Y., & Ma, S. (2020). Efficient Neural Matrix Factorization without Sampling for Recommendation. ACM Transactions on Information Systems, 38(2), Article 14. https://doi.org/10.1145/3373807 Go to original source...
    6. Chen, X., Yang, G., Yang, S., Wang, H., Dong, S., & Gao, Y. (2023). Online attentive kernel-based temporal difference learning. Knowledge-Based Systems, 278, 110902. https://doi.org/10.1016/j.knosys.2023.110902 Go to original source...
    7. Cui, Y., Yu, H., Guo, X., Cao, H., & Wang, L. (2024). RAKCR: Reviews sentiment-aware based knowledge graph convolutional networks for Personalized Recommendation. Expert Systems With Applications, 248, 123403. https://doi.org/10.1016/j.eswa.2024.123403 Go to original source...
    8. Darbandi, M. (2017). Proposing new intelligent system for suggesting better service providers in cloud computing based on Kalman filtering. HCTL Open International Journal of Technology Innovations and Research, 24, 1-9.
    9. Dilekh, T., Benharzallah, S., Mokeddem, A., & Kerdoudi, S. (2024). Dynamic Context-Aware Recommender System for home automation through synergistic unsupervised and supervised learning algorithms. Acta Informatica Pragensia, 13(1), 38-61. https://doi.org/10.18267/j.aip.228 Go to original source...
    10. Engström, E., Vartanova, I., Johansson, J. V., Persson, M., & Strimling, P. (2024). Comparing and modeling the use of online recommender systems. Computers in Human Behavior Reports, 15, 100449. https://doi.org/10.1016/j.chbr.2024.100449 Go to original source...
    11. Guo, F., Wang, Z., Wang, X., Lu, Q., & Ji, S. (2024). Dual-view multi-modal contrastive learning for graph-based recommender systems. Computers & Electrical Engineering, 116, 109213. https://doi.org/10.1016/j.compeleceng.2024.109213 Go to original source...
    12. Halder, S., Lim, K. H., Chan, J., & Zhang, X. (2024). A survey on personalized itinerary recommendation: From optimisation to deep learning. Applied Soft Computing, 152, 111200. https://doi.org/10.1016/j.asoc.2023.111200 Go to original source...
    13. Heidari, N., Moradi, P., & Koochari, A. (2022). An attention-based deep learning method for solving the cold-start and sparsity issues of recommender systems. Knowledge-Based Systems, 256, 109835. https://doi.org/10.1016/j.knosys.2022.109835 Go to original source...
    14. Henriques, R., & Pinto, L. (2023). A novel evaluation framework for recommender systems in big data environments. Expert Systems With Applications, 231, 120659. https://doi.org/10.1016/j.eswa.2023.120659 Go to original source...
    15. Hsu, C. (2024). Does a gamified website matter? An extended UTAUT model with regulatory fit, user experience, engagement, and attitudes. Entertainment Computing, 50, 100658. https://doi.org/10.1016/j.entcom.2024.100658 Go to original source...
    16. Hu, Y., Xiong, F., Pan, S., Xiong, X., Wang, L., & Chen, H. (2021). Bayesian personalized ranking based on multiple-layer neighborhoods. Information Sciences, 542, 156-176. https://doi.org/10.1016/j.ins.2020.06.067 Go to original source...
    17. Huang, W., Hao, F., Shang, J., Yu, W., Zeng, S., Bisogni, C., & Loia, V. (2023). Dual-LightGCN: Dual light graph convolutional network for discriminative recommendation. Computer Communications, 204, 89-100. https://doi.org/10.1016/j.comcom.2023.03.018 Go to original source...
    18. Kannout, E., Grzegorowski, M., Grodzki, M., & Nguyen, H. S. (2024). Clustering-Based frequent pattern mining framework for solving Cold-Start problem in recommender systems. IEEE Access, 12, 13678-13698. https://doi.org/10.1109/access.2024.3355057 Go to original source...
    19. Kipf, T. N, & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations 2017. https://openreview.net/pdf?id=SJU4ayYgl
    20. Lee, S., Tanveer, J., Rahmani, A. M., Alinejad-Rokny, H., Khoshvaght, P., Zare, G., Alamdari, P. M., & Hosseinzadeh, M. (2025). SFGCN: Synergetic Fusion-based Graph Convolutional Networks Approach for link prediction in social networks. Information Fusion, 114, 102684. https://doi.org/10.1016/j.inffus.2024.102684 Go to original source...
    21. Liu, C., Yu, C., Gui, N., Yu, Z., & Deng, S. (2024). SimGCL: graph contrastive learning by finding homophily in heterophily. Knowledge and Information Systems, 66(3), 2089-2114. https://doi.org/10.1007/s10115-023-02022-1 Go to original source...
    22. Padhye, V., & Lakshmanan, K. (2023). A deep actor critic reinforcement learning framework for learning to rank. Neurocomputing, 547, 126314. https://doi.org/10.1016/j.neucom.2023.126314 Go to original source...
    23. Saini, K., & Singh, A. (2024). A content-based recommender system using stacked LSTM and an attention-based autoencoder. Measurement Sensors, 31, 100975. https://doi.org/10.1016/j.measen.2023.100975 Go to original source...
    24. Salvi, M., Loh, H. W., Seoni, S., Barua, P. D., García, S., Molinari, F., & Acharya, U. R. (2024). Multi-modality approaches for medical support systems: A systematic review of the last decade. Information Fusion, 103, 102134. https://doi.org/10.1016/j.inffus.2023.102134 Go to original source...
    25. Schulman, J., Levine, S., Moritz, P., Jordan, M., I., & Abbeel, P. (2015). Trust Region Policy optimization. arXiv. https://doi.org/10.48550/arxiv.1502.05477 Go to original source...
    26. Steck, H. (2019). Embarrassingly Shallow Autoencoders for Sparse Data. In WWW '19: The World Wide Web Conference, (pp. 3251-3257). ACM. https://doi.org/10.1145/3308558.3313710 Go to original source...
    27. Sutton, R. S. (2018). Reinforcement learning: An introduction. Bradford Book.
    28. Sutton, R. S, McAllester, D., Singh, S., & Mansour, Y. (1999). Policy gradient methods for reinforcement learning with function approximation. In NIPS'99: Proceedings of the 13th International Conference on Neural Information Processing Systems, (pp. 1057-1063). NIPS.
    29. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2017). Graph attention networks. arXiv. https://doi.org/10.48550/arxiv.1710.10903 Go to original source...
    30. Wang, X., He, X., Wang, M., Feng, F., & Chua, T. (2019). Neural Graph Collaborative Filtering. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, (pp. 165-174). ACM. https://doi.org/10.1145/3331184.3331267 Go to original source...
    31. Wei, T., Chow, T. W., Ma, J., & Zhao, M. (2023). ExpGCN: Review-aware Graph Convolution Network for explainable recommendation. Neural Networks, 157, 202-215. https://doi.org/10.1016/j.neunet.2022.10.014 Go to original source...
    32. Wu, J., Wang, X., Feng, F., He, X., Chen, L., Lian, J., & Xie, X. (2021). Self-supervised graph learning for recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, (pp. 726-735). ACM. https://doi.org/10.1145/3404835.3462862 Go to original source...
    33. Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2018). How Powerful are Graph Neural Networks? arXiv. https://doi.org/10.48550/arxiv.1810.00826 Go to original source...
    34. Zare, G. (2025). A Deep Reinforcement Learning-Based Approach for Improving the Accuracy of the Recommendations in Social Networks. Doctoral dissertation. Islamic Azad University, Qeshm Branch.
    35. Zare, G., Navimipour, N. J., Hosseinzadeh, M., & Sahafi, A. (2024). Network link prediction via deep learning method: A comparative analysis with traditional methods. Engineering Science and Technology an International Journal, 56, 101782. https://doi.org/10.1016/j.jestch.2024.101782 Go to original source...
    36. Zhang, J., Zain, A. M., Zhou, K., Chen, X., & Zhang, R. (2024). A review of recommender systems based on knowledge graph embedding. Expert Systems With Applications, 250, 123876. https://doi.org/10.1016/j.eswa.2024.123876 Go to original source...
    37. Zhang, Y., Zhu, J., Zhang, Y., Zhu, Y., Zhou, J., & Xie, Y. (2024). Social-aware graph contrastive learning for recommender systems. Applied Soft Computing, 158, 111558. https://doi.org/10.1016/j.asoc.2024.111558 Go to original source...
    38. Zhao, W. X., Hou, Y., Pan, X., Yang, C., Zhang, Z., Lin, Z., Zhang, J., Bian, S., Tang, J., Sun, W., Chen, Y., Xu, L., Zhang, G., Tian, Z., Tian, C., Mu, S., Fan, X., Chen, X., & Wen, J. (2022). RecBole 2.0: Towards a More Up-to-Date Recommendation Library. In Proceedings of the 31st ACM International Conference on Information and Knowledge Management (pp. 4722-4726). ACM. https://doi.org/10.1145/3511808.3557680 Go to original source...
    39. Zhao, Z., Tong, X., Wang, Y., & Zhang, Q. (2024). Multi-behavior contrastive learning with graph neural networks for recommendation. Knowledge-Based Systems, 300, 112221. https://doi.org/10.1016/j.knosys.2024.112221 Go to original source...
    40. Zhou, T., Ye, H., & Cao, F. (2024). Node-personalized multi-graph convolutional networks for recommendation. Neural Networks, 173, 106169. https://doi.org/10.1016/j.neunet.2024.106169 Go to original source...
    41. Zhu, Y., Ma, J., Yuan, C., & Zhu, X. (2021). Interpretable learning based Dynamic Graph Convolutional Networks for Alzheimer's Disease analysis. Information Fusion, 77, 53-61. https://doi.org/10.1016/j.inffus.2021.07.013 Go to original source...

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